ICDAR 2011 Document Image Binarization Contest (DIBCO 2011)

Pratikakis, Ioannis; Gatos, Basilis; Ntirogiannis, Konstantinos

doi:10.1109/icdar.2011.299

Cited by 167 publications

(83 citation statements)

References 11 publications

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“…The resulting cluster sizes are monitored until the size threshold on the FG clusters (t sz = 2% of total image size) is met as indicated in STEP [4]- [7]. The binarized image is generated by marking BG pixels in black and FG in white (STEP [8]) as it will be used to preserve all the edges (STEP [4] with thresholds t hi indicated in Howe's algorithm [4]) with at least one pixel classified as FG by saving all the 8-connected neighbours of FG pixels along the edges (STEP [9]). The graph-cut was done on the binarized image acting as source-sink prior guided by the modified edges with their respective weights (W ss = 0.66, W edge = 25.0).…”

Section: Methodsmentioning

confidence: 99%

Document Binarization Using Topological Clustering Guided Laplacian Energy Segmentation

Ayyalasomayajula

Brun

2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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Abstract-The current approach for text binarization proposes a clustering algorithm as a preprocessing stage to an energy-based segmentation method. It uses a clustering algorithm to obtain a coarse estimate of the background (BG) and foreground (FG) pixels. These estimates are used as a prior for the source and sink points of a graph cut implementation, which is used to efficiently find the minimum energy solution of an objective function to separate the BG and FG. The binary image thus obtained is used to refine the edge map that guides the graph cut algorithm. A final binary image is obtained by once again performing the graph cut guided by the refined edges on a Laplacian of the image.

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Section: Methodsmentioning

confidence: 99%

Document Binarization Using Topological Clustering Guided Laplacian Energy Segmentation

Ayyalasomayajula

Brun

2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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“…7 with the help of PSNR and MSE values [2].A higher value of PSNR indicates that the algorithm under consideration has enhanced a degraded image to more closely resemble the original image.A low value of MSE indicates that the error by which the original image differs from degraded image is less. …”

Section: Performance Evaluationmentioning

confidence: 99%

“…This method is simple. But the limitation of this method is that it does not work properly on degraded document images with a complex background [2]. Local maximum minimum method [7] is an improvement over Bernsen's method and handles the documents with a complex background well.…”

Section: Related Workmentioning

confidence: 99%

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Document Image Binarization Technique for Degraded Document Images

Lokhande¹,

Dawande²

2015

IJCA

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“…This technique could be applied to both even and odd numbers of binary images. The benchmark data set and four evaluation measures from DIBCO 2011 's report [5] were used. The proposed method has been applied to generate the optimal binarised output with practical data set which has been collected by Project for Palm Leaf Preservation in Northeastern Thailand Division, Mahasarakham University [6].…”

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confidence: 99%

Generation of optimal binarisation output from ancient Thai manuscripts on palm leaves

Chamchong

Jareanpon

Fung

2013

2013 International Conference on Machine Learning and Cybernetics

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Abstract:Recently, several binarisation techniques have been proposed to process different kinds of ancient document images. While many well-known binarisation techniques are particularly suitable for certain types of document images, there is no specific guidelines on the determination of the appropriate type of image degradation, or characteristics of the image. In this paper, a novel method has been proposed to generate the optimal binary image from different binarised outputs from a document image. This approach is based on weight majority vote, and uncertain pixels are then determined based on local areas of the binarised images, by applying iteration of weight majority vote. Experiment over benchmark data set of the Document Image Binarization Contest (DIBCO) 2011 shows that the proposed method provided better performance than most well-known techniques. The proposed method has also been applied to ancient manuscripts on palm leaves from Thailand and this approach provided better results than binarised outputs from original binarisation techniques.

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ICDAR 2011 Document Image Binarization Contest (DIBCO 2011)

Cited by 167 publications

References 11 publications

Document Binarization Using Topological Clustering Guided Laplacian Energy Segmentation

Document Binarization Using Topological Clustering Guided Laplacian Energy Segmentation

Document Image Binarization Technique for Degraded Document Images

Generation of optimal binarisation output from ancient Thai manuscripts on palm leaves

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